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Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2869463
Abstract: Graphs are ubiquitous, and graph analytics has been widely adopted in many big data applications such as social computation and natural language processing, as well as web-search and recommendation systems. Prior research focuses on processing…
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Keywords:
manycore memory;
analytics manycore;
memory systems;
graph analytics ... See more keywords
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Published in 2022 at "IEEE Transactions on Computers"
DOI: 10.1109/tc.2021.3057860
Abstract: Graph analytics are an emerging class of irregular applications. Operating on very large datasets, they present unique behaviors, such as fine-grained, unpredictable memory accesses, and highly unbalanced task level parallelism, that make existing high-performance general-purpose…
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Keywords:
level;
level synthesis;
accelerators graph;
graph analytics ... See more keywords
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Published in 2018 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2018.2807452
Abstract: Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot…
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Keywords:
comprehensive survey;
graph embedding;
survey graph;
graph ... See more keywords
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Published in 2019 at "IEEE Transactions on Parallel and Distributed Systems"
DOI: 10.1109/tpds.2019.2899595
Abstract: Graph analytics has been routinely used to solve problems in a wide range of real-life applications. Efficiently processing concurrent graph analytics queries in a multiuser environment is highly desirable as we enter a world of…
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Keywords:
memory bandwidth;
processing concurrent;
concurrent graph;
graph ... See more keywords